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ORCIDAlexander G. de G. Matthews
Person information
- affiliation: DeepMind
- affiliation (former): University of Cambridge, Department of Engineering
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2020 – today
- 2023
[i9]Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar
, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban:
Normalizing flows for lattice gauge theory in arbitrary space-time dimension. CoRR abs/2305.02402 (2023)- 2022
- [c9]Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. ICML 2022: 15196-15219 - [c8]Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann:
Score-Based Diffusion meets Annealed Importance Sampling. NeurIPS 2022 - [i8]Alexander G. de G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. CoRR abs/2201.13117 (2022) - [i7]Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann:
Score-Based Diffusion meets Annealed Importance Sampling. CoRR abs/2208.07698 (2022) - [i6]Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban:
Aspects of scaling and scalability for flow-based sampling of lattice QCD. CoRR abs/2211.07541 (2022) - 2021
- [c7]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. ICML 2021: 318-330 - [i5]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. CoRR abs/2102.07501 (2021) - 2020
- [c6]Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh:
Functional Regularisation for Continual Learning with Gaussian Processes. ICLR 2020
2010 – 2019
- 2019
- [i4]Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh:
Functional Regularisation for Continual Learning using Gaussian Processes. CoRR abs/1901.11356 (2019) - [i3]David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. Matthew C. Foulkes:
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. CoRR abs/1909.02487 (2019) - 2018
- [c5]Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. ICLR (Poster) 2018 - [c4]Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani:
Variational Bayesian dropout: pitfalls and fixes. ICML 2018: 2024-2033 - [i2]Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR abs/1804.11271 (2018) - [i1]Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani:
Variational Bayesian dropout: pitfalls and fixes. CoRR abs/1807.01969 (2018) - 2017
- [j1]Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman:
GPflow: A Gaussian Process Library using TensorFlow. J. Mach. Learn. Res. 18: 40:1-40:6 (2017) - 2016
- [c3]Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani:
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. AISTATS 2016: 231-239 - 2015
- [c2]James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani:
Scalable Variational Gaussian Process Classification. AISTATS 2015 - [c1]James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani:
MCMC for Variationally Sparse Gaussian Processes. NIPS 2015: 1648-1656
Coauthor Index
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